You're reading from Causal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781804612989

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Data Science

Author (1):

Aleksander Molak

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Table of Contents (21) Chapters

Preface

1. Part 1: Causality – an Introduction

2. Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother? FREE CHAPTER

3. Chapter 2: Judea Pearl and the Ladder of Causation

4. Chapter 3: Regression, Observations, and Interventions

5. Chapter 4: Graphical Models

6. Chapter 5: Forks, Chains, and Immoralities

7. Part 2: Causal Inference

8. Chapter 6: Nodes, Edges, and Statistical (In)dependence

9. Chapter 7: The Four-Step Process of Causal Inference

10. Chapter 8: Causal Models – Assumptions and Challenges

11. Chapter 9: Causal Inference and Machine Learning – from Matching to Meta-Learners

12. Chapter 10: Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More

13. Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond

14. Part 3: Causal Discovery

15. Chapter 12: Can I Have a Causal Graph, Please?

16. Chapter 13: Causal Discovery and Machine Learning – from Assumptions to Applications

17. Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond

18. Chapter 15: Epilogue

19. Index

20. Other Books You May Enjoy

DAG your pardon? Directed acyclic graphs in the causal wonderland

We’ll start this section by reviewing definitions of causality. Then, we’ll discuss the motivations behind DAGs and their limitations. Finally, we’ll formalize the concept of a DAG.

Definitions of causality

In the first chapter, we discussed a couple of historical definitions of causality. We started with Aristotle, then we briefly covered the ideas proposed by David Hume. We’ve seen that Hume’s definition (as we presented it) was focused on associations. This led us to look into how babies learn about the world using experimentation. We‘ve seen how experimentation allows us to go beyond the realm of observations by interacting with the environment. The possibility of interacting with the environment is at the heart of another definition of causality that comes from Judea Pearl.

Pearl proposed something very simple yet powerful. His definition is short, ignores ontological...

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You're reading from Causal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Table of Contents (21) Chapters

DAG your pardon? Directed acyclic graphs in the causal wonderland

Definitions of causality

Authors (1)

Personalised recommendations for you

You're reading from Causal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Table of Contents (21) Chapters

DAG your pardon? Directed acyclic graphs in the causal wonderland

Definitions of causality

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you